基于希尔伯特—黄变换的故障转子振动模式分析方法研究
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摘要
本论文结合国家高技术研究发展计划(863)项目“超临界、超超临界大型汽轮发电机组状态监测与故障诊断技术及其系统研究”(编号：2008AA04Z410)和国家自然科学基金项目“基于经验模式分解的旋转机械振动模式分析方法的研究”(编号：11072214),开展了故障转子在典型故障条件下振动模式分析方法的研究。论文定义了包含故障转子系统固有频率的固有模式函数(IMF)为故障振动模式,构建了转子系统典型故障模拟试验平台,并研究了基于改进希尔伯特-黄变换(HHT)的不对中、动静件碰摩故障转子振动模式提取方法。进行了转子典型故障的分类方法研究。开发了基于改进HHT的超临界汽轮发电机组状态监测软件,并进行了现场数据分析。
论文的主要研究工作和章节安排如下：
第1章分析了转子振动信号特征提取方法的研究现状,并将HHT方法引入转子振动信号特征提取中。阐述了经验模式分解(EMD)、HHT的研究现状,分析了HHT用于转子振动信号特征提取时存在的不足。分析了转子典型故障识别方法,并论证了模态分析在故障识别中的适用性。建立了基于HHT的故障转子振动模式分析框架。最后给出了本文的选题背景、研究内容、创新点和总体框架。
第2章分析了转子系统故障诊断的一般流程,指出了引入模态分析方法进行故障诊断的必要性。定义了包含故障转子系统固有频率的IMF分量为故障振动模式,并给出了故障振动模式分析的一般流程。构建了转子系统典型故障模拟试验系统,用于理论方法的试验验证。提出了一种基于CTSA-HWT-RDT的运行模态分析(OMA)技术,成功提取系统的前几阶固有频率。通过检测IMF中包含的系统前几阶固有频率,确定了IMF与旋转机械的振动模式分量(正常振动模式和故障振动模式)之间存在的对应关系。以动静件碰摩故障为例,通过OMA方法提取故障转子的固有频率,并对响应进行EMD分解,发现了故障转子系统固有频率在各阶IMF中分布的变化规律。
第3章对不对中故障机理进行了分析。针对不对中转子系统亚谐波共振条件下在频域范围内的特征与裂纹转子类似的特点,提出了一种在全频率(FS)域内的不对中故障特征提取新方法。针对不对中转子系统非亚谐波共振条件下故障特征不明显的特点,提出了一种基于多维EMD (MEMD)固有频率提取和轴系振型分析的不对中故障振动模式分析方法。转子试验台的试验结果表明了方法的有效性。
第4章针对原始HHT在处理频率成分连续而丰富的信号时,无法准确估计信号瞬时频率的不足,提出了一种基于能量占优滤波HHT (BF-HHT)的故障特征提取新方法,该方法能够准确提取原信号高频范围内的瞬时频率特征。研究了一种基于谱峭度HHT (SK-HHT)的故障非平稳性检测方法,用于复杂多频信号特征提取。转子试验台的试验结果表明了方法的有效性。
第5章分析了支持向量机(SVM)的小样本数据分类优势。针对全周碰摩和局部碰摩故障的时频域特征相似,传统时频域分析方法很难提取有效故障特征的问题,研究了一种基于EMD-SVD-SVM的全周碰摩与局部碰摩故障识别方法。针对不对中、裂纹、初始弯曲等转子系统典型故障的故障振动模式分布规律类似的特点,研究了一种基于MEMD-ICA和SVM的特征向量获取与分类方法。
第6章阐述了所开发的基于改进HHT的超临界汽轮发电机组状态监测软件系统,并使用该系统对工业现场数据进行了测试与分析,验证了本文所提方法的工程适用性。
第7章给出了本文研究的主要结论,并对今后研究工作提出了展望。
This thesis, in which the research work focused on the topic of faulty vibration mode analysis of rotor system, was mainly supported by the High Technology Research and Development Program of China'Research on the condition monitoring and fault diagnosis technique and its application software system of supercritical and ultra-supercritical steam turbine generator unit'(No.2008AA04Z410) and the National Natural Fund Project'Research on the analysis of vibration mode for rotating machinery under typical fault based on EMD'(No.11072214). This study consists of the definition of the vibration mode of a fault rotor system according to the Intrinsic mode function (IMF), the installation of the experiment system, the research of principles on the vibration mode of a rotor system under misalignment and rotor-to-stator rub fault, and the development of new time-frequency extraction methods based on improved Hilbert-Huang transform (HHT). All the theoretical approaches listed above were testified by the industrial data obtained from the developed software.
The scheme of this study is organized as follows:
In Chapter1, rotor's fault feature extraction methods together with their advantages and disadvantages, were firstly discussed. After the analysis of the significance for the introduction of HHT based feature extraction method, the deficiency of HHT and the necessity for the utilization of modal analysis were presented. The support vector machine (SVM) was here applied to the identification for the typical rotor faults as it has several advantages in the analysis of small fault sample data. As the traditional modal analysis methods have their deficiencies in rotating machinery modal analysis, utilization of operational modal analysis (OMA) was further suggested for fault extraction. Based upon the above analysis, the framework of HHT based fault mode analysis, together with the background, the innovation points and the scheme of this thesis was given.
In Chapter2, all the existing fault diagnosis methods were compared from the fault mechanism side. After the definition of fault vibration mode and the introduction of the test-bench, the basic principles of EMD, together with the significance of interpretating Intrinsic mode function's (IMF's) physical meanings was addressed. The suitability of interpretating IMF's physical meaning using OMA technique is also proved. Studying the vibration mode of a typical rotor fault, i.e., rotor-to-stator rub, led to the proposal of the general process for extracting typical rotor fault vibration modes.
In Chapter3, the state-of-art of the rotor misalignment fault was reviewed and all the existing fault diagnosis methods were compared. Comparisons impelled the findings of feature extraction problems both under the sub-harmonic and non-sub-harmonic resonance condition. Thus based upon the similarity of frequency composition between misaligned and cracked rotor under the sub-harmonic resonance condition, a full spectrum analysis method was proposed. Also, a newly developed natural frequency extraction method was proposed to make up for the deficiency of non-sub-harmonic feature extraction.
In Chapter4, the state-of-art of the rotor-to-stator rub fault was reviewed and all the existing fault diagnosis methods were compared. Based on the analysis, certain improvement had to be made for the original HHT. The improvement is achieved by a newly developed time-frequency analysis tools, named the bandpass filtered HHT (BF-HHT). With this tool, accurate instantaneous frequency can be obtained, which do great help to characterize highly nonstationary signals. Aslo, a new time-frequency extraction procedure, named the spectral kurtosis HHT (SK-HHT), was proposed to automatically extract the most nonstationary frequency components in a signal, in which the nonstationary components usually mean some fault that had happened in the rotor system.
In Chapter5, reviewing of the existing pattern recognition methods was firstly made. Considering the advantages of SVM to analyze small sample data, SVM is extremely suitable to be applied to analyze hardly accessible fault data. Study founds out that their exist two major difficulties in fault identification. One is the discrimination between full annular rub and local rub fault. This chapter proposed a effective solution to solve this problem based on EMD-SVD-SVM. The other difficulty is the discrimination of high-feature-frequency characterized faults, i.e., misalignment, crack and initial bow. Based upon the feature extraction of such faults, a new identification procedure is proposed based on Multivariate EMD (MEMD), independent component analysis (ICA) and SVM.
In Chapter6, the constitution of condition monitoring software using HHT for supercritical steam turbine generator unit was firstly introduced. Furthermore, the industrial on-line data was obtained and analyzed by this software using the above analysis methods.
In Chapter7, the conclusions were made and some advanced topics that need further investigation were presented.
论文的主要研究工作和章节安排如下：
第1章分析了转子振动信号特征提取方法的研究现状,并将HHT方法引入转子振动信号特征提取中。阐述了经验模式分解(EMD)、HHT的研究现状,分析了HHT用于转子振动信号特征提取时存在的不足。分析了转子典型故障识别方法,并论证了模态分析在故障识别中的适用性。建立了基于HHT的故障转子振动模式分析框架。最后给出了本文的选题背景、研究内容、创新点和总体框架。
第2章分析了转子系统故障诊断的一般流程,指出了引入模态分析方法进行故障诊断的必要性。定义了包含故障转子系统固有频率的IMF分量为故障振动模式,并给出了故障振动模式分析的一般流程。构建了转子系统典型故障模拟试验系统,用于理论方法的试验验证。提出了一种基于CTSA-HWT-RDT的运行模态分析(OMA)技术,成功提取系统的前几阶固有频率。通过检测IMF中包含的系统前几阶固有频率,确定了IMF与旋转机械的振动模式分量(正常振动模式和故障振动模式)之间存在的对应关系。以动静件碰摩故障为例,通过OMA方法提取故障转子的固有频率,并对响应进行EMD分解,发现了故障转子系统固有频率在各阶IMF中分布的变化规律。
第3章对不对中故障机理进行了分析。针对不对中转子系统亚谐波共振条件下在频域范围内的特征与裂纹转子类似的特点,提出了一种在全频率(FS)域内的不对中故障特征提取新方法。针对不对中转子系统非亚谐波共振条件下故障特征不明显的特点,提出了一种基于多维EMD (MEMD)固有频率提取和轴系振型分析的不对中故障振动模式分析方法。转子试验台的试验结果表明了方法的有效性。
第4章针对原始HHT在处理频率成分连续而丰富的信号时,无法准确估计信号瞬时频率的不足,提出了一种基于能量占优滤波HHT (BF-HHT)的故障特征提取新方法,该方法能够准确提取原信号高频范围内的瞬时频率特征。研究了一种基于谱峭度HHT (SK-HHT)的故障非平稳性检测方法,用于复杂多频信号特征提取。转子试验台的试验结果表明了方法的有效性。
第5章分析了支持向量机(SVM)的小样本数据分类优势。针对全周碰摩和局部碰摩故障的时频域特征相似,传统时频域分析方法很难提取有效故障特征的问题,研究了一种基于EMD-SVD-SVM的全周碰摩与局部碰摩故障识别方法。针对不对中、裂纹、初始弯曲等转子系统典型故障的故障振动模式分布规律类似的特点,研究了一种基于MEMD-ICA和SVM的特征向量获取与分类方法。
第6章阐述了所开发的基于改进HHT的超临界汽轮发电机组状态监测软件系统,并使用该系统对工业现场数据进行了测试与分析,验证了本文所提方法的工程适用性。
第7章给出了本文研究的主要结论,并对今后研究工作提出了展望。
This thesis, in which the research work focused on the topic of faulty vibration mode analysis of rotor system, was mainly supported by the High Technology Research and Development Program of China'Research on the condition monitoring and fault diagnosis technique and its application software system of supercritical and ultra-supercritical steam turbine generator unit'(No.2008AA04Z410) and the National Natural Fund Project'Research on the analysis of vibration mode for rotating machinery under typical fault based on EMD'(No.11072214). This study consists of the definition of the vibration mode of a fault rotor system according to the Intrinsic mode function (IMF), the installation of the experiment system, the research of principles on the vibration mode of a rotor system under misalignment and rotor-to-stator rub fault, and the development of new time-frequency extraction methods based on improved Hilbert-Huang transform (HHT). All the theoretical approaches listed above were testified by the industrial data obtained from the developed software.
The scheme of this study is organized as follows:
In Chapter1, rotor's fault feature extraction methods together with their advantages and disadvantages, were firstly discussed. After the analysis of the significance for the introduction of HHT based feature extraction method, the deficiency of HHT and the necessity for the utilization of modal analysis were presented. The support vector machine (SVM) was here applied to the identification for the typical rotor faults as it has several advantages in the analysis of small fault sample data. As the traditional modal analysis methods have their deficiencies in rotating machinery modal analysis, utilization of operational modal analysis (OMA) was further suggested for fault extraction. Based upon the above analysis, the framework of HHT based fault mode analysis, together with the background, the innovation points and the scheme of this thesis was given.
In Chapter2, all the existing fault diagnosis methods were compared from the fault mechanism side. After the definition of fault vibration mode and the introduction of the test-bench, the basic principles of EMD, together with the significance of interpretating Intrinsic mode function's (IMF's) physical meanings was addressed. The suitability of interpretating IMF's physical meaning using OMA technique is also proved. Studying the vibration mode of a typical rotor fault, i.e., rotor-to-stator rub, led to the proposal of the general process for extracting typical rotor fault vibration modes.
In Chapter3, the state-of-art of the rotor misalignment fault was reviewed and all the existing fault diagnosis methods were compared. Comparisons impelled the findings of feature extraction problems both under the sub-harmonic and non-sub-harmonic resonance condition. Thus based upon the similarity of frequency composition between misaligned and cracked rotor under the sub-harmonic resonance condition, a full spectrum analysis method was proposed. Also, a newly developed natural frequency extraction method was proposed to make up for the deficiency of non-sub-harmonic feature extraction.
In Chapter4, the state-of-art of the rotor-to-stator rub fault was reviewed and all the existing fault diagnosis methods were compared. Based on the analysis, certain improvement had to be made for the original HHT. The improvement is achieved by a newly developed time-frequency analysis tools, named the bandpass filtered HHT (BF-HHT). With this tool, accurate instantaneous frequency can be obtained, which do great help to characterize highly nonstationary signals. Aslo, a new time-frequency extraction procedure, named the spectral kurtosis HHT (SK-HHT), was proposed to automatically extract the most nonstationary frequency components in a signal, in which the nonstationary components usually mean some fault that had happened in the rotor system.
In Chapter5, reviewing of the existing pattern recognition methods was firstly made. Considering the advantages of SVM to analyze small sample data, SVM is extremely suitable to be applied to analyze hardly accessible fault data. Study founds out that their exist two major difficulties in fault identification. One is the discrimination between full annular rub and local rub fault. This chapter proposed a effective solution to solve this problem based on EMD-SVD-SVM. The other difficulty is the discrimination of high-feature-frequency characterized faults, i.e., misalignment, crack and initial bow. Based upon the feature extraction of such faults, a new identification procedure is proposed based on Multivariate EMD (MEMD), independent component analysis (ICA) and SVM.
In Chapter6, the constitution of condition monitoring software using HHT for supercritical steam turbine generator unit was firstly introduced. Furthermore, the industrial on-line data was obtained and analyzed by this software using the above analysis methods.
In Chapter7, the conclusions were made and some advanced topics that need further investigation were presented.
引文
[1]何正嘉,陈进,王太勇,等.机械故障诊断理论及应用[M].北京：高等教育出版社,2010.
[2]吴昭同,杨世锡.旋转机械故障特征提取与模式分类新方法[M].北京：科学出版社,2012.
[3]沃兹·海伦,斯蒂芬·拉门兹,波尔·萨斯.模态分析理论与试验[M].北京：北京理工大学出版社,2001.
[4]Reynders E. System Identification Methods for (Operational) Modal Analysis:Review and Comparison[J]. Archives of Computational Methods in Engineering,2012,19(1):51-124.
[5]Patel T H, Darpe A K. Experimental investigations on vibration response of misaligned rotors[J]. Mechanical Systems and Signal Processing,2009,23(7):2236-2252.
[6]樊永生.机械设备的现代信号处理方法[M].北京：国防工业出版社,2009.
[7]张清华,邵龙秋,李红芳,等.基于无量纲指标的旋转机械并发故障诊断技术[J].华中科技大学学报(自然科学版),2009,37(S1)：156-159.
[8]张清华,钱宇,胥布工,等.无量纲指标及人工免疫系统故障诊断技术的应用研究[J].噪声与振动控制,2008,(1)：89-92.
[9]余先涛,余明辉.相关分析在刚性转子动平衡测试中的应用[J].武汉大学学报(工学版),2001,34(4)：100-103.
[10]丁康,李巍华,朱小勇.齿轮及齿轮箱故障诊断实用技术[M].北京：机械工业出版社,2005.
[11]丁康,谢明,张彼德,等.基于复解析带通滤波器的复调制细化谱分析原理和方法[J].振动工程学报,2001,14(1)：33-39.
[12]田昊,唐力伟,陈红,等.基于瞬态声与阶次倒谱的齿轮箱故障诊断[J].振动、测试与诊断,2009,29(2)：137-140,237-238.
[13]Lihui F, Hui L, Yaning W. Application of Bi-cepstrum Analysis to Gear Fault Detection and Diagnosis[C]. In:International Conference on Measuring Technology and Mechatronics Automation,2009,590-593.
[14]Combet F, Gelman L. Novel adaptation of the demodulation technology for gear damage detection to variable amplitudes of mesh harmonics[J]. Mechanical Systems and Signal Processing, 2011,25(3):839-845.
[15]Hyvarinen A, Karhunen J, Oja E独立分量分析[M].北京：电子工业出版社,2007.
[16]Comon P. Independent component analysis, A new concept?[J]. Signal Processing,1994,36(3): 287-314.
[17]Jutten C, Comon P. Chapter 1 - Introduction, in Handbook of Blind Source Separation[M]. P. Comon and C. Jutten, Editors.2010, Academic Press:Oxford, p.1-22.
[18]Tsoi A C, Ma L S. Blind deconvolution of dynamical systems using a balanced parameterized state space approach[C]. In:Proceedings of the 2003 IEEE International Conference on Acoustics, Speech and Signal Processing,2003,4:309-312.
[19]Ypma A, Leshem A, P.W. Duin R. Blind separation of rotating machine sources:bilinear forms and convolutive mixtures[J]. Neurocomputing,2002,49(1-4):349-368.
[20]Gelle G, Colas M, Serviere C. Blind source separation:a tool for rotating machine monitoring by vibrations analysis [J]? Journal of Sound and Vibration,2001,248(5):865-885.
[21]焦卫东.基于独立分量分析的旋转机械故障诊断方法研究[D].淅江大学,2003.
[22]叶红仙.机械系统振动源的盲分离方法研究[D].浙江大学,2008.
[23]屈梁生.机械故障的全息诊断原理[M].北京：科学出版社,2007.
[24]Antoni J. Blind separation of vibration components:Principles and demonstrations[J]. Mechanical Systems and Signal Processing,2005,19(6):1166-1180.
[25]Arabaci H, Bilgin O. Neural Network Classification and Diagnosis of Broken Rotor Bar Faults by Means of Short Time Fourier Transform[C]. In:International Multi-Conference of Engineers and Computer Scientists,2009, Vol I:219-223.
[26]Arabaci H, Bilgin O. The detection of rotor faults by using Short Time Fourier Transform[M]. 2007 IEEE 15th Signal Processing and Communications Applications, Vols 1-3:648-651,2007.
[27]胡劲松,杨世锡,吴昭同,等.旋转机械振动信号基于EMD的HT和STFT时频分析比较[J].汽轮机技术,2002,44(6)：336-338.
[28]Climente-Alarcon V, Antonino-Daviu J A, Riera M, et al. Wigner-Ville Distribution for the Detection of High-order Harmonics due to Rotor Asymmetries[M].2009 IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives,119-124,2009.
[29]Zou J, Chen J, Niu J C, et al. Application of the Wigner-Ville distribution to identification of a cracked rotor[J]. Proceedings of the Institution of Mechanical Engineers Part C-Journal of Mechanical Engineering Science,2003,217(5):551-556.
[30]杨福生.小波变换的工程分析与应用[M].北京：科学出版社,1999.
[31]Hou S M, Li Y R, Wang Z G. A Resonance Demodulation Method Based on Harmonic Wavelet Transform for Rolling Bearing Fault Diagnosis[J]. Structural Health Monitoring-an International Journal,2010,9(4):297-308.
[32]Mao Y F, Qin S R, Qin Y. Demodulation Based on Harmonic Wavelet and Its Application into Rotary Machinery Fault Diagnosis[J]. Chinese Journal of Mechanical Engineering,2009,22(3): 419-425.
[33]刘小峰,彭永金,李慧.谐波小波解调法在齿轮箱故障诊断中的应用[J].重庆大学学报,201 1,34(1)：15-20.
[34]杨江天,周培钰.经验模态分解和Laplace小波在机车柴油机齿轮系故障诊断中的应用[J].机械工程学报,2011,47(7)：109-115.
[35]Sweldens W. The Lifting Scheme:A Custom-Design Construction of Biorthogonal Wavelets[J]. Applied and Computational Harmonic Analysis,1996,3(2):186-200.
[36]唐贵基,王誉蓉,郭新富,等.基于第二代小波变换的振动信号去噪与故障诊断[J].汽轮机技术,2006,48(4)：295-297.
[37]Li N, Zhou R, Hu Q H, et al. Mechanical fault diagnosis based on redundant second generation wavelet packet transform, neighborhood rough set and support vector machine[J]. Mechanical Systems and Signal Processing,2012,28:608-621.
[38]Zhou R, Bao W, Li N, et al. Mechanical equipment fault diagnosis based on redundant second generation wavelet packet transform[J]. Digital Signal Processing,2010,20(1):276-288.
[39]Huang N E, Shen Z, Long S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proceedings of the Royal Society of London. Series A:Mathematical, Physical and Engineering Sciences,1998,454(1971):903-995.
[40]Kaslovsky D N, Meyer F G. Noise corruption of empirical mode decomposition and its effect on instantaneous frequency[J]. Advances in Adaptive Data Analysis (AADA),2010,2(3):373-396.
[41]Duffy D G. The Application of Hilbert-Huang Transforms to Meteorological Datasets[J]. Journal of Atmospheric and Oceanic Technology,2004,21(4):599-611.
[42]Coughlin K T, Tung K. K.11-Year solar cycle in the stratosphere extracted by the empirical mode decomposition method[J]. Advances in Space Research,2004,34(2):323-329.
[43]Pinzon J E, Pierce J F, Tucker C J, et al. Evaluating coherence of natural images by smoothness membership in Besov spaces[J]. Geoscience and Remote Sensing, IEEE Transactions on,2001, 39(9):1879-1889.
[44]Hwang P A, Wang D W. Field Measurements of Duration-Limited Growth of Wind-Generated Ocean Surface Waves at Young Stage of Development* [J]. Journal of Physical Oceanography,2004, 34(10):2316-2326.
[45]Pines D, Salvino L. Structural health monitoring using empirical mode decomposition and the Hilbert phase[J]. Journal of Sound and Vibration,2006,294(1-2):97-124.
[46]Li Y F, Tseng W C, Huang K, et al. A study of ambient vibration test of bridge column by using Hilbert-Huang transform[J]. J. Chinese Inst. Civ. Hydr. Eng,2003,15:21-33.
[47]Nunes J C, Bouaoune Y, Delechelle E, et al. Image analysis by bidimensional empirical mode decomposition[J]. Image and Vision Computing,2003,21(12):1019-1026.
[48]Gravier B. An assessment of the application of the Hilbert spectrum to the fatigue analysis of marine risers.[C]. In Proceedings of the International offshore and polar engineering conference, 2001,268-275.
[49]Rai V K, Mohanty A R. Bearing fault diagnosis using FFT of intrinsic mode functions in Hilbert-Huang transform[J]. Mechanical Systems and Signal Processing,2007,21(6):2607-2615.
[50]Parey A, Tandon N. Impact velocity modelling and signal processing of spur gear vibration for the estimation of defect size[J]. Mechanical Systems and Signal Processing,2007,21(1):234-243.
[51]Loutridis S J. Instantaneous energy density as a feature for gear fault detection[J]. Mechanical Systems and Signal Processing,2006,20(5):1239-1253.
[52]雷亚国,何正嘉,訾艳阳,等.基于特征评估和神经网络的机械故障诊断模型[J].西安交通大学学报,2006,40(5)：558-562.
[53]盖强,张海勇,徐晓刚.Hilbert-Huang变换的自适应频率多分辨分析研究[J].电子学报,2005,33(3)：563-566.
[54]沈国际,陶利民,陈仲生.多频信号经验模态分解的理论研究及应用[J].振动工程学报,2005,18(1)：96-99.
[55]胡劲松,杨世锡.基于自相关的旋转机械振动信号EMD分解方法研究[J].机械强度,2007,29(3)：376-379.
[56]熊忻,杨世锡,甘春标.基于BF-HHT的转子径向碰摩故障特征提取方法[J].振动、测试与诊断,2012,32(2)：192-196.
[57]Peng Z K, Tse P W, Chu F L. An improved Hilbert-Huang transform and its application in vibration signal analysis[J]. Journal of Sound and Vibration,2005,286(1-2):187-205.
[58]曹冲锋.基于EMD的机械振动分析与诊断方法研究[D].浙江大学,2009.
[59]莫平杰,杨世锡,曹冲锋.振动模态固有频率和阻尼比的EMD识别方法[J].机电工程,2011,28(4)：392-396,428.
[60]哈姆,科斯塔尼克.神经计算原理[M].北京：机械工业出版社,2007.
[61]Manson G, Worden K, Allman D. Experimental validation of a structural health monitoring methodology:Part Ⅱ. Novelty detection on a GNAT aircraft[J]. Journal of Sound and Vibration, 2003,259(2):345-363.
[62]Zhang Z, Friedrich K. Artificial neural networks applied to polymer composites:a review[J]. Composites Science and Technology,2003,63(14):2029-2044.
[63]Markou M, Singh S. Novelty detection:a review—part 2::neural network based approaches[J]. Signal Processing,2003,83(12):2499-2521.
[64]Ding Y, Song X, Zen Y. Forecasting financial condition of Chinese listed companies based on support vector machine[J]. Expert Systems with Applications,2008,34(4):3081-3089.
[65]Luo Y-L, Xu W-J. Inner damage of aeroengine recognition based on support vector machines[M]. Proceedings of 2006 Chinese Control and Decision Conference, ed. S.Y.W.F. Zhang 245-248,2006.
[66]Poyhonen S, Negrea M, Arkkio A, et al. Fault diagnostics of an electrical machine with multiple support vector classifiers[M]. Proceedings of the 2002 Ieee International Symposium on Intelligent Control 373-378,2002.
[67]司刚全,娄勇,张寅松.鲁棒最小二乘支持向量机及其在软测量中的应用[J].西安交通大学学报,2012,46(8)：15-21.
[68]辛治运,顾明.基于最小二乘支持向量机的复杂金融时间序列预测[J].清华大学学报(自然科学版),2008,48(7)：1147-1149.
[69]张超,陈建军,郭迅.基于EEMD能量熵和支持向量机的齿轮故障诊断方法[J].中南大学学报(自然科学版),2012,43(3)：932-939.
[70]康守强,王玉静,杨广学,等.基于经验模态分解和超球多类支持向量机的滚动轴承故障诊断方法[J].中国电机工程学报,2011,31(14)：96-102.
[71]张力,林建龙,项辉宇.模态分析与实验[M].北京：清华大学出版社,2011.
[72]韩清凯,于涛,孙伟.机械振动系统的现代动态设计与分析[M].北京：科学出版社,2010.
[73]杨为,邱清盈,胡建军.机械结构的理论模态分析方法[J].重庆大学学报(自然科学版),2004,27(6)：1-4.
[74]Harik I E, Madasamy C M, Chen D, et al. Modal analysis of long span truss bridges, in Imac -Proceedings of the 17th International Modal Analysis Conference, Vols I and Ii.1999. p. 1244-1250.
[75]Zhang J J, Zhong J T, He L L, et al. Modal Analysis of the Cable-Stayed Space Truss Combining ANSYS and MSC.ADAMS, in Nanotechnology and Computer Engineering, D.C. Wunsch, et al., Editors.2010. p.832-837.
[76]Yuan S X, Wen X L, Zhang Y M. Modal Analysis on the Truss Structures of Machine Tool, in Materials and Product Technologies, L.Y. Xie, et al., Editors.2010. p.972-976.
[77]刘祥伟,陈昆,梅杰,等.新型塔架门式起重机整体结构有限元分析[J].武汉理工大学学报(信息与管理工程版),2012,34(1)：38-41.
[78]王学文,杨兆建,王义亮.大型电除尘器钢结构宽大梁有限元分析[J].太原理工大学学报,2004,35(2)：160-163.
[79]Bhartiya Y, Sinha A. Reduced Order Model of a Multistage Bladed Rotor With Geometric Mistuning via Modal Analyses of Finite Element Sectors[J]. Journal of Turbomachinery-Transactions of the Asme,2012,134(4).
[80]Hua X, Lim T C, Peng T, et al. Dynamic analysis of spiral bevel geared rotor systems applying finite elements and enhanced lumped parameters[J]. International Journal of Automotive Technology,2012,13(1):97-107.
[81]Lee S M, Lee W J. Finite-element analysis on thermornechanical behavior of a marine propeller casting in the sand-casting process[J]. Journal of Materials Engineering and Performance,2005, 14(3):388-394.
[82]Estep D J, Hodges D H, Warner M. The solution of a launch vehicle trajectory problem by an adaptive finite-element method[J]. Computer Methods in Applied Mechanics and Engineering, 2001,190(35-36):4677-4690.
[83]Roh W, Kim Y. Trajectory optimization for a multi-stage launch vehicle using time finite element and direct collocation methods[J]. Engineering Optimization,2002,34(1):15-32.
[84]郭建龙,陈世元.外转子双凸极永磁电动机的有限元分析[J].中国电机工程学报,2007,27(36)：46-51.
[85]周宗和,杨自春.基于积分随机有限元法的汽轮机转子随机响应特性分析[J].中国电机工程学报,2011,31(2)：67-72.
[86]Abdelghani M, Goursat M, Biolchini T. On-line modal monitoring of aircraft structures under unknown excitation[J]. Mechanical Systems and Signal Processing,1999,13(6):839-853.
[87]张令弥.模态试验与分析的发展(三)——试验模态分析系统[J].振动与冲击,1989,29(1)：52-60.
[88]Allen M S, Floquet experimental modal analysis for system identification of linear time-periodic systems[M]. Proceedings of the Asme International Design Engineering Technical Conferences and Computers and Information in Engineering Conference 2007, Vol 5, Pts a-C 823-833,2008.
[89]Cai Y, Weng Z, Zhang N, et al. Experimental Modal Analysis of CNC High Precision Surface Grinder with Horizontal Spindle and Rotary Table MGK7350, in Manufacturing Science and Engineering, Pts 1-5, Z.Z.C.L. Jiang, Editor.2010. p.3637-3641.
[90]Chen C H, Ou C I. Experimental modal test and time-domain aerodynamic analysis of a cable-stayed bridge[J]. International Journal of Structural Stability and Dynamics,2011,11(1): 101-125.
[91]Kreuzinger-Janik T, Irretier H, Imeche I. Unbalance identification of flexible rotors based on experimental modal analysis, in Seventh International Conference on Vibrations in Rotating Machinery.2000. p.335-346.
[92]Mariani R, Dessi D, Katholieke Univ Leuven D W. Experimental modal analysis of a ship structure based on the proper orthogonal decomposition[M]. Proceedings of Isma 2008: International Conference on Noise and Vibration Engineering, Vols.1-8 2507-2521,2008.
[93]Ren W X, Zhao T, Harik I E. Experimental and analytical modal analysis of steel arch bridge[J]. Journal of Structural Engineering-Asce,2004,130(7):1022-1031.
[94]Thomas M, Nguyen H, Hamidi L, et al. Detection of rotor cracks by experimental modal-analysis[J]. Transactions of the Canadian Society for Mechanical Engineering,1995,19(2): 155-174.
[95]Wang Y Y, Ji S M, Wen D H, et al. Experimental and Numerical Modal Analysis of Gearbox Casing in a Polishing Machinery, in Ultra-Precision Machining Technologies, J.L.J.S.M.W.D.H.C.M. Yuan, Editor.2009. p.560-564.
[96]Lyon R H. Machinery noise and diagnostics[M]. Boston:Butterworths,1987.
[97]刘锦阳,任平珍,廖明夫,等.柔性转子不平衡响应及初始弯曲振动特性研究[J].振动、测试与诊断,1998,18(4)：50-54,76..
[98]罗挺,刘淑莲,郑水英.具有初始弯曲的多圆盘转子系统的动力学特性分析[C].第9届全国转子动力学学术讨论会,2010,174-176.
[99]刘健,刁训林,潘怀美.转子热态不平衡振动故障的研究与消除[J].机电产品开发与创新,2008,21(1)：56-57,63.
[100]Al-Hussain K. M, Redmond I. Dynamic response of two rotors connected by rigid mechanical coupling with parallel misalignment[J]. Journal of Sound and Vibration,2002,249(3):483-498.
[101]Lees A W. Misalignment in rigidly coupled rotors[J]. Journal of Sound and Vibration,2007, 305(1-2):261-271.
[102]Bouaziz S, Attia Hili M, Mataar M, et al. Dynamic behaviour of hydrodynamic journal bearings in presence of rotor spatial angular misalignment[J]. Mechanism and Machine Theory,2009,44(8): 1548-1559.
[103]Saavedra P N, Ramirez D E. Vibration analysis of rotors for the identification of shaft misalignment Part 2:experimental validation[J]. Proceedings of the Institution of Mechanical Engineers Part C-Journal of Mechanical Engineering Science,2004,218(9):987-999.
[104]Ming X, Kang D. Corrections for frequency, amplitude and phase in a fast Fourier transform of harmonic signal[J]. Mechanical Systems and Signal Processing,1996,10(2):211-221.
[105]Newland D E. Harmonic Wavelet Analysis[J]. Proceedings of the Royal Society of London. Series A:Mathematical and Physical Sciences,1993,443(1917):203-225.
[106]Asmussen J C, Brincker R, Ibrahim S R. STATISTICAL THEORY OF THE VECTOR RANDOM DECREMENT TECHNIQUE[J]. Journal of Sound and Vibration,1999,226(2): 329-344.
[107]Goldman P, Muszynska A. Application of full spectrum to rotating machinery diagnostics[J]. Orbit,1999, First Quarter:17-21.
[108]Fengqi W, Meng G. Compound rub malfunctions feature extraction based on full-spectrum cascade analysis and SVM[J]. Mechanical Systems and Signal Processing,2006,20(8):2007-2021.
[109]Patel T H, Darpe A K. Vibration response of a cracked rotor in presence of rotor-stator rub[J]. Journal of Sound and Vibration,2008,317(3-5):841-865.
[110]Rehman N, Mandic D P. Multivariate empirical mode decomposition[J]. Proceedings of the Royal Society A:Mathematical, Physical and Engineering Science,2009,0502:1471-2946.
[111]Niederreiter H. Random Number Generation and Quasi-Monte Carlo Methods[M]. Philadelphia: PA:Society for Industrial and Applied Mathematics,1992.
[112]Xiong X, Yang S, Gan C. A new procedure for extracting fault feature of multi-frequency signal from rotating machinery[J]. Mechanical Systems and Signal Processing,32:306-319.
[113]Bently D E, Yu J J, Goldman P, et al. Full Annular RUB in Mechanical Seals, Part I: Experimental Results[J]. International Journal of Rotating Machinery,2002,8(5):319-328.
[114]Choi Y-S. Dynamics of rotor rub in annular clearance with experimental evaluation [J]. Journal of Mechanical Science and Technology,1994,8(4):404-413.
[115]徐尉南,张文,许斌.柔性转子-柔性静子系统的同步全周碰摩分析[J].振动与冲击,2007,26(2)：1-5,9,171.
[116]Pennacchi P, Bachschmid N, Tanzi E. Light and short arc rubs in rotating machines: Experimental tests and modelling[J]. Mechanical Systems and Signal Processing,2009,23(7): 2205-2227.
[117]王正浩,袁惠群,范改燕.考虑轴初弯曲时转子系统局部碰摩的分叉与混沌行为[J].机械设计,2008,25(2)：3741.
[118]Cheng J, Yu D, Tang J, et al. Local rub-impact fault diagnosis of the rotor systems based on EMD[J]. Mechanism and Machine Theory,2009,44(4):784-791.
[119]熊炘,杨世锡,周晓峰.旋转机械振动信号的固有模式函数降噪方法[J].浙江大学学报(工学版),2011,45(8)：1376-1381.
[120]Huang N E, Wu Z, Long S R, et al. On instantaneous frequency[J]. Advances in Adaptive Data Analysis (AADA),2009,1(2):177-229.
[121]Antoni J. The spectral kurtosis:a useful tool for characterising non-stationary signals[J]. Mechanical Systems and Signal Processing,2006,20(2):282-307.
[122]Antoni J. Fast computation of the kurtogram for the detection of transient faults[J]. Mechanical Systems and Signal Processing,2007,21(1):108-124.
[123]Sohre J. Operating Problems with High-Speed Turbomachinery-Causes and Correction, in 23rd Annual Petroleum Mechanical Engineering Conference.1968:Denver, Colorado.
[124]Venkatasubramanian V, Rengaswamy R, Yin K, et al. A review of process fault detection and diagnosis part I:Quantitative model-based methods[J]. Computers and Chemical Engineering,2003, 27(3):293-311.
[125]钟秉林,黄仁.机械故障诊断学[M].北京：机械工业出版社,2006.
[126]陈长征,胡立新,周勃,等.设备振动分析与故障诊断技术[M].北京：科学出版社,2007.
[127]Nieddu L, Patrizi G. Formal methods in pattern recognition:A review[J]. European Journal of Operational Research,2000,120(3):459-495.
[128]Jain A K, Duin R P W, Mao J C. Statistical pattern recognition:A review[J]. leee Transactions on Pattern Analysis and Machine Intelligence,2000,22(1):4-37.
[129]Worden K, Staszewski W J, Hensman J J. Natural computing for mechanical systems research:A tutorial overview[J]. Mechanical Systems and Signal Processing,2011,25(1):4-111.
[130]Dahl G E, Yu D, Deng L, et al. Context-Dependent Pre-Trained Deep Neural, Networks for Large-Vocabulary Speech Recognition[J]. leee Transactions on Audio Speech and Language Processing,2012,20(1):30-42.
[131]Lin J C, Wu C H, Wei W L. Error Weighted Semi-Coupled Hidden Markov Model for Audio-Visual Emotion Recognition [J]. Ieee Transactions on Multimedia,2012,14(1):142-156.
[132]Slosarz P, Stanisz M, Boniecki P, et al. Artificial neural network analysis of ultrasound image for the estimation of intramuscular fat content in lamb muscle[J]. African Journal of Biotechnology, 2011,10(55):11792-11796.
[133]Madani K, leee. Artificial Neural Networks Based Image Processing & Pattern Recognition: From Concepts to Real-World Applications[M].2008 First International Workshops on Image Processing Theory, Tools and Applications 19-27,2008.
[134]Murthy V S, Gupta S, Mohanta D K. Digital image processing approach using combined wavelet hidden Markov model for well-being analysis of insulators[J]. let Image Processing,2011, 5(2):171-183.
[135]Ju Z J, Liu H H, Zhu X Y, et al. Dynamic Grasp Recognition Using Time Clustering, Gaussian Mixture Models and Hidden Markov Models[J]. Advanced Robotics,2009,23(10):1359-1371.
[136]Khan F N, Zhou Y D, Lau A P T, et al. Modulation format identification in heterogeneous fiber-optic networks using artificial neural networks[J]. Optics Express,2012,20(11): 12422-12431.
[137]Jin X, Huang B. Identification of switched Markov autoregressive eXogenous systems with hidden switching state[J]. Automatica,2012,48(2):436-441.
[138]Das N, Reddy J M, Sarkar R, et al. A statistical-topological feature combination for recognition of handwritten numerals[J]. Applied Soft Computing,2012,12(8):2486-2495.
[139]Solera-Urena R, Garcia-Moral A I, Pelaez-Moreno C, et al. Real-Time Robust Automatic Speech Recognition Using Compact Support Vector Machines[J]. Ieee Transactions on Audio Speech and Language Processing,2012,20(4):1347-1361.
[140]Wang B H, Huang H J, Wang X L. A novel text mining approach to financial time series forecasting[J]. Neurocomputing,2012,83:136-145.
[141]Fong S. Using Hierarchical Time Series Clustering Algorithm and Wavelet Classifier for Biometric Voice Classification[J]. Journal of Biomedicine and Biotechnology,2012,1-12.
[142]Widodo A, Yang B S. Support vector machine in machine condition monitoring and fault diagnosis[J]. Mechanical Systems and Signal Processing,2007,21(6):2560-2574.
[143]Wang Y, Kang S, Jiang Y, et al. Classification of fault location and the degree of performance degradation of a rolling bearing based on an improved hyper-sphere-structured multi-class support vector machine[J]. Mechanical Systems and Signal Processing,2012,29(0):404-414.
[144]MUSZYNSKA A. Rotor-to-stationary element rub-related vibration phenomena in rotating machinery:Literature survey[J]. The Shock and Vibration Digest,1989,21(3):3-11.
[145]Wang Q H, Zhang Y Y, Cai L, et al. Fault diagnosis for diesel valve trains based on non-negative matrix factorization and neural network ensemble[J]. Mechanical Systems and Signal Processing, 2009,23(5):1683-1695.
[2]吴昭同,杨世锡.旋转机械故障特征提取与模式分类新方法[M].北京：科学出版社,2012.
[3]沃兹·海伦,斯蒂芬·拉门兹,波尔·萨斯.模态分析理论与试验[M].北京：北京理工大学出版社,2001.
[4]Reynders E. System Identification Methods for (Operational) Modal Analysis:Review and Comparison[J]. Archives of Computational Methods in Engineering,2012,19(1):51-124.
[5]Patel T H, Darpe A K. Experimental investigations on vibration response of misaligned rotors[J]. Mechanical Systems and Signal Processing,2009,23(7):2236-2252.
[6]樊永生.机械设备的现代信号处理方法[M].北京：国防工业出版社,2009.
[7]张清华,邵龙秋,李红芳,等.基于无量纲指标的旋转机械并发故障诊断技术[J].华中科技大学学报(自然科学版),2009,37(S1)：156-159.
[8]张清华,钱宇,胥布工,等.无量纲指标及人工免疫系统故障诊断技术的应用研究[J].噪声与振动控制,2008,(1)：89-92.
[9]余先涛,余明辉.相关分析在刚性转子动平衡测试中的应用[J].武汉大学学报(工学版),2001,34(4)：100-103.
[10]丁康,李巍华,朱小勇.齿轮及齿轮箱故障诊断实用技术[M].北京：机械工业出版社,2005.
[11]丁康,谢明,张彼德,等.基于复解析带通滤波器的复调制细化谱分析原理和方法[J].振动工程学报,2001,14(1)：33-39.
[12]田昊,唐力伟,陈红,等.基于瞬态声与阶次倒谱的齿轮箱故障诊断[J].振动、测试与诊断,2009,29(2)：137-140,237-238.
[13]Lihui F, Hui L, Yaning W. Application of Bi-cepstrum Analysis to Gear Fault Detection and Diagnosis[C]. In:International Conference on Measuring Technology and Mechatronics Automation,2009,590-593.
[14]Combet F, Gelman L. Novel adaptation of the demodulation technology for gear damage detection to variable amplitudes of mesh harmonics[J]. Mechanical Systems and Signal Processing, 2011,25(3):839-845.
[15]Hyvarinen A, Karhunen J, Oja E独立分量分析[M].北京：电子工业出版社,2007.
[16]Comon P. Independent component analysis, A new concept?[J]. Signal Processing,1994,36(3): 287-314.
[17]Jutten C, Comon P. Chapter 1 - Introduction, in Handbook of Blind Source Separation[M]. P. Comon and C. Jutten, Editors.2010, Academic Press:Oxford, p.1-22.
[18]Tsoi A C, Ma L S. Blind deconvolution of dynamical systems using a balanced parameterized state space approach[C]. In:Proceedings of the 2003 IEEE International Conference on Acoustics, Speech and Signal Processing,2003,4:309-312.
[19]Ypma A, Leshem A, P.W. Duin R. Blind separation of rotating machine sources:bilinear forms and convolutive mixtures[J]. Neurocomputing,2002,49(1-4):349-368.
[20]Gelle G, Colas M, Serviere C. Blind source separation:a tool for rotating machine monitoring by vibrations analysis [J]? Journal of Sound and Vibration,2001,248(5):865-885.
[21]焦卫东.基于独立分量分析的旋转机械故障诊断方法研究[D].淅江大学,2003.
[22]叶红仙.机械系统振动源的盲分离方法研究[D].浙江大学,2008.
[23]屈梁生.机械故障的全息诊断原理[M].北京：科学出版社,2007.
[24]Antoni J. Blind separation of vibration components:Principles and demonstrations[J]. Mechanical Systems and Signal Processing,2005,19(6):1166-1180.
[25]Arabaci H, Bilgin O. Neural Network Classification and Diagnosis of Broken Rotor Bar Faults by Means of Short Time Fourier Transform[C]. In:International Multi-Conference of Engineers and Computer Scientists,2009, Vol I:219-223.
[26]Arabaci H, Bilgin O. The detection of rotor faults by using Short Time Fourier Transform[M]. 2007 IEEE 15th Signal Processing and Communications Applications, Vols 1-3:648-651,2007.
[27]胡劲松,杨世锡,吴昭同,等.旋转机械振动信号基于EMD的HT和STFT时频分析比较[J].汽轮机技术,2002,44(6)：336-338.
[28]Climente-Alarcon V, Antonino-Daviu J A, Riera M, et al. Wigner-Ville Distribution for the Detection of High-order Harmonics due to Rotor Asymmetries[M].2009 IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives,119-124,2009.
[29]Zou J, Chen J, Niu J C, et al. Application of the Wigner-Ville distribution to identification of a cracked rotor[J]. Proceedings of the Institution of Mechanical Engineers Part C-Journal of Mechanical Engineering Science,2003,217(5):551-556.
[30]杨福生.小波变换的工程分析与应用[M].北京：科学出版社,1999.
[31]Hou S M, Li Y R, Wang Z G. A Resonance Demodulation Method Based on Harmonic Wavelet Transform for Rolling Bearing Fault Diagnosis[J]. Structural Health Monitoring-an International Journal,2010,9(4):297-308.
[32]Mao Y F, Qin S R, Qin Y. Demodulation Based on Harmonic Wavelet and Its Application into Rotary Machinery Fault Diagnosis[J]. Chinese Journal of Mechanical Engineering,2009,22(3): 419-425.
[33]刘小峰,彭永金,李慧.谐波小波解调法在齿轮箱故障诊断中的应用[J].重庆大学学报,201 1,34(1)：15-20.
[34]杨江天,周培钰.经验模态分解和Laplace小波在机车柴油机齿轮系故障诊断中的应用[J].机械工程学报,2011,47(7)：109-115.
[35]Sweldens W. The Lifting Scheme:A Custom-Design Construction of Biorthogonal Wavelets[J]. Applied and Computational Harmonic Analysis,1996,3(2):186-200.
[36]唐贵基,王誉蓉,郭新富,等.基于第二代小波变换的振动信号去噪与故障诊断[J].汽轮机技术,2006,48(4)：295-297.
[37]Li N, Zhou R, Hu Q H, et al. Mechanical fault diagnosis based on redundant second generation wavelet packet transform, neighborhood rough set and support vector machine[J]. Mechanical Systems and Signal Processing,2012,28:608-621.
[38]Zhou R, Bao W, Li N, et al. Mechanical equipment fault diagnosis based on redundant second generation wavelet packet transform[J]. Digital Signal Processing,2010,20(1):276-288.
[39]Huang N E, Shen Z, Long S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proceedings of the Royal Society of London. Series A:Mathematical, Physical and Engineering Sciences,1998,454(1971):903-995.
[40]Kaslovsky D N, Meyer F G. Noise corruption of empirical mode decomposition and its effect on instantaneous frequency[J]. Advances in Adaptive Data Analysis (AADA),2010,2(3):373-396.
[41]Duffy D G. The Application of Hilbert-Huang Transforms to Meteorological Datasets[J]. Journal of Atmospheric and Oceanic Technology,2004,21(4):599-611.
[42]Coughlin K T, Tung K. K.11-Year solar cycle in the stratosphere extracted by the empirical mode decomposition method[J]. Advances in Space Research,2004,34(2):323-329.
[43]Pinzon J E, Pierce J F, Tucker C J, et al. Evaluating coherence of natural images by smoothness membership in Besov spaces[J]. Geoscience and Remote Sensing, IEEE Transactions on,2001, 39(9):1879-1889.
[44]Hwang P A, Wang D W. Field Measurements of Duration-Limited Growth of Wind-Generated Ocean Surface Waves at Young Stage of Development* [J]. Journal of Physical Oceanography,2004, 34(10):2316-2326.
[45]Pines D, Salvino L. Structural health monitoring using empirical mode decomposition and the Hilbert phase[J]. Journal of Sound and Vibration,2006,294(1-2):97-124.
[46]Li Y F, Tseng W C, Huang K, et al. A study of ambient vibration test of bridge column by using Hilbert-Huang transform[J]. J. Chinese Inst. Civ. Hydr. Eng,2003,15:21-33.
[47]Nunes J C, Bouaoune Y, Delechelle E, et al. Image analysis by bidimensional empirical mode decomposition[J]. Image and Vision Computing,2003,21(12):1019-1026.
[48]Gravier B. An assessment of the application of the Hilbert spectrum to the fatigue analysis of marine risers.[C]. In Proceedings of the International offshore and polar engineering conference, 2001,268-275.
[49]Rai V K, Mohanty A R. Bearing fault diagnosis using FFT of intrinsic mode functions in Hilbert-Huang transform[J]. Mechanical Systems and Signal Processing,2007,21(6):2607-2615.
[50]Parey A, Tandon N. Impact velocity modelling and signal processing of spur gear vibration for the estimation of defect size[J]. Mechanical Systems and Signal Processing,2007,21(1):234-243.
[51]Loutridis S J. Instantaneous energy density as a feature for gear fault detection[J]. Mechanical Systems and Signal Processing,2006,20(5):1239-1253.
[52]雷亚国,何正嘉,訾艳阳,等.基于特征评估和神经网络的机械故障诊断模型[J].西安交通大学学报,2006,40(5)：558-562.
[53]盖强,张海勇,徐晓刚.Hilbert-Huang变换的自适应频率多分辨分析研究[J].电子学报,2005,33(3)：563-566.
[54]沈国际,陶利民,陈仲生.多频信号经验模态分解的理论研究及应用[J].振动工程学报,2005,18(1)：96-99.
[55]胡劲松,杨世锡.基于自相关的旋转机械振动信号EMD分解方法研究[J].机械强度,2007,29(3)：376-379.
[56]熊忻,杨世锡,甘春标.基于BF-HHT的转子径向碰摩故障特征提取方法[J].振动、测试与诊断,2012,32(2)：192-196.
[57]Peng Z K, Tse P W, Chu F L. An improved Hilbert-Huang transform and its application in vibration signal analysis[J]. Journal of Sound and Vibration,2005,286(1-2):187-205.
[58]曹冲锋.基于EMD的机械振动分析与诊断方法研究[D].浙江大学,2009.
[59]莫平杰,杨世锡,曹冲锋.振动模态固有频率和阻尼比的EMD识别方法[J].机电工程,2011,28(4)：392-396,428.
[60]哈姆,科斯塔尼克.神经计算原理[M].北京：机械工业出版社,2007.
[61]Manson G, Worden K, Allman D. Experimental validation of a structural health monitoring methodology:Part Ⅱ. Novelty detection on a GNAT aircraft[J]. Journal of Sound and Vibration, 2003,259(2):345-363.
[62]Zhang Z, Friedrich K. Artificial neural networks applied to polymer composites:a review[J]. Composites Science and Technology,2003,63(14):2029-2044.
[63]Markou M, Singh S. Novelty detection:a review—part 2::neural network based approaches[J]. Signal Processing,2003,83(12):2499-2521.
[64]Ding Y, Song X, Zen Y. Forecasting financial condition of Chinese listed companies based on support vector machine[J]. Expert Systems with Applications,2008,34(4):3081-3089.
[65]Luo Y-L, Xu W-J. Inner damage of aeroengine recognition based on support vector machines[M]. Proceedings of 2006 Chinese Control and Decision Conference, ed. S.Y.W.F. Zhang 245-248,2006.
[66]Poyhonen S, Negrea M, Arkkio A, et al. Fault diagnostics of an electrical machine with multiple support vector classifiers[M]. Proceedings of the 2002 Ieee International Symposium on Intelligent Control 373-378,2002.
[67]司刚全,娄勇,张寅松.鲁棒最小二乘支持向量机及其在软测量中的应用[J].西安交通大学学报,2012,46(8)：15-21.
[68]辛治运,顾明.基于最小二乘支持向量机的复杂金融时间序列预测[J].清华大学学报(自然科学版),2008,48(7)：1147-1149.
[69]张超,陈建军,郭迅.基于EEMD能量熵和支持向量机的齿轮故障诊断方法[J].中南大学学报(自然科学版),2012,43(3)：932-939.
[70]康守强,王玉静,杨广学,等.基于经验模态分解和超球多类支持向量机的滚动轴承故障诊断方法[J].中国电机工程学报,2011,31(14)：96-102.
[71]张力,林建龙,项辉宇.模态分析与实验[M].北京：清华大学出版社,2011.
[72]韩清凯,于涛,孙伟.机械振动系统的现代动态设计与分析[M].北京：科学出版社,2010.
[73]杨为,邱清盈,胡建军.机械结构的理论模态分析方法[J].重庆大学学报(自然科学版),2004,27(6)：1-4.
[74]Harik I E, Madasamy C M, Chen D, et al. Modal analysis of long span truss bridges, in Imac -Proceedings of the 17th International Modal Analysis Conference, Vols I and Ii.1999. p. 1244-1250.
[75]Zhang J J, Zhong J T, He L L, et al. Modal Analysis of the Cable-Stayed Space Truss Combining ANSYS and MSC.ADAMS, in Nanotechnology and Computer Engineering, D.C. Wunsch, et al., Editors.2010. p.832-837.
[76]Yuan S X, Wen X L, Zhang Y M. Modal Analysis on the Truss Structures of Machine Tool, in Materials and Product Technologies, L.Y. Xie, et al., Editors.2010. p.972-976.
[77]刘祥伟,陈昆,梅杰,等.新型塔架门式起重机整体结构有限元分析[J].武汉理工大学学报(信息与管理工程版),2012,34(1)：38-41.
[78]王学文,杨兆建,王义亮.大型电除尘器钢结构宽大梁有限元分析[J].太原理工大学学报,2004,35(2)：160-163.
[79]Bhartiya Y, Sinha A. Reduced Order Model of a Multistage Bladed Rotor With Geometric Mistuning via Modal Analyses of Finite Element Sectors[J]. Journal of Turbomachinery-Transactions of the Asme,2012,134(4).
[80]Hua X, Lim T C, Peng T, et al. Dynamic analysis of spiral bevel geared rotor systems applying finite elements and enhanced lumped parameters[J]. International Journal of Automotive Technology,2012,13(1):97-107.
[81]Lee S M, Lee W J. Finite-element analysis on thermornechanical behavior of a marine propeller casting in the sand-casting process[J]. Journal of Materials Engineering and Performance,2005, 14(3):388-394.
[82]Estep D J, Hodges D H, Warner M. The solution of a launch vehicle trajectory problem by an adaptive finite-element method[J]. Computer Methods in Applied Mechanics and Engineering, 2001,190(35-36):4677-4690.
[83]Roh W, Kim Y. Trajectory optimization for a multi-stage launch vehicle using time finite element and direct collocation methods[J]. Engineering Optimization,2002,34(1):15-32.
[84]郭建龙,陈世元.外转子双凸极永磁电动机的有限元分析[J].中国电机工程学报,2007,27(36)：46-51.
[85]周宗和,杨自春.基于积分随机有限元法的汽轮机转子随机响应特性分析[J].中国电机工程学报,2011,31(2)：67-72.
[86]Abdelghani M, Goursat M, Biolchini T. On-line modal monitoring of aircraft structures under unknown excitation[J]. Mechanical Systems and Signal Processing,1999,13(6):839-853.
[87]张令弥.模态试验与分析的发展(三)——试验模态分析系统[J].振动与冲击,1989,29(1)：52-60.
[88]Allen M S, Floquet experimental modal analysis for system identification of linear time-periodic systems[M]. Proceedings of the Asme International Design Engineering Technical Conferences and Computers and Information in Engineering Conference 2007, Vol 5, Pts a-C 823-833,2008.
[89]Cai Y, Weng Z, Zhang N, et al. Experimental Modal Analysis of CNC High Precision Surface Grinder with Horizontal Spindle and Rotary Table MGK7350, in Manufacturing Science and Engineering, Pts 1-5, Z.Z.C.L. Jiang, Editor.2010. p.3637-3641.
[90]Chen C H, Ou C I. Experimental modal test and time-domain aerodynamic analysis of a cable-stayed bridge[J]. International Journal of Structural Stability and Dynamics,2011,11(1): 101-125.
[91]Kreuzinger-Janik T, Irretier H, Imeche I. Unbalance identification of flexible rotors based on experimental modal analysis, in Seventh International Conference on Vibrations in Rotating Machinery.2000. p.335-346.
[92]Mariani R, Dessi D, Katholieke Univ Leuven D W. Experimental modal analysis of a ship structure based on the proper orthogonal decomposition[M]. Proceedings of Isma 2008: International Conference on Noise and Vibration Engineering, Vols.1-8 2507-2521,2008.
[93]Ren W X, Zhao T, Harik I E. Experimental and analytical modal analysis of steel arch bridge[J]. Journal of Structural Engineering-Asce,2004,130(7):1022-1031.
[94]Thomas M, Nguyen H, Hamidi L, et al. Detection of rotor cracks by experimental modal-analysis[J]. Transactions of the Canadian Society for Mechanical Engineering,1995,19(2): 155-174.
[95]Wang Y Y, Ji S M, Wen D H, et al. Experimental and Numerical Modal Analysis of Gearbox Casing in a Polishing Machinery, in Ultra-Precision Machining Technologies, J.L.J.S.M.W.D.H.C.M. Yuan, Editor.2009. p.560-564.
[96]Lyon R H. Machinery noise and diagnostics[M]. Boston:Butterworths,1987.
[97]刘锦阳,任平珍,廖明夫,等.柔性转子不平衡响应及初始弯曲振动特性研究[J].振动、测试与诊断,1998,18(4)：50-54,76..
[98]罗挺,刘淑莲,郑水英.具有初始弯曲的多圆盘转子系统的动力学特性分析[C].第9届全国转子动力学学术讨论会,2010,174-176.
[99]刘健,刁训林,潘怀美.转子热态不平衡振动故障的研究与消除[J].机电产品开发与创新,2008,21(1)：56-57,63.
[100]Al-Hussain K. M, Redmond I. Dynamic response of two rotors connected by rigid mechanical coupling with parallel misalignment[J]. Journal of Sound and Vibration,2002,249(3):483-498.
[101]Lees A W. Misalignment in rigidly coupled rotors[J]. Journal of Sound and Vibration,2007, 305(1-2):261-271.
[102]Bouaziz S, Attia Hili M, Mataar M, et al. Dynamic behaviour of hydrodynamic journal bearings in presence of rotor spatial angular misalignment[J]. Mechanism and Machine Theory,2009,44(8): 1548-1559.
[103]Saavedra P N, Ramirez D E. Vibration analysis of rotors for the identification of shaft misalignment Part 2:experimental validation[J]. Proceedings of the Institution of Mechanical Engineers Part C-Journal of Mechanical Engineering Science,2004,218(9):987-999.
[104]Ming X, Kang D. Corrections for frequency, amplitude and phase in a fast Fourier transform of harmonic signal[J]. Mechanical Systems and Signal Processing,1996,10(2):211-221.
[105]Newland D E. Harmonic Wavelet Analysis[J]. Proceedings of the Royal Society of London. Series A:Mathematical and Physical Sciences,1993,443(1917):203-225.
[106]Asmussen J C, Brincker R, Ibrahim S R. STATISTICAL THEORY OF THE VECTOR RANDOM DECREMENT TECHNIQUE[J]. Journal of Sound and Vibration,1999,226(2): 329-344.
[107]Goldman P, Muszynska A. Application of full spectrum to rotating machinery diagnostics[J]. Orbit,1999, First Quarter:17-21.
[108]Fengqi W, Meng G. Compound rub malfunctions feature extraction based on full-spectrum cascade analysis and SVM[J]. Mechanical Systems and Signal Processing,2006,20(8):2007-2021.
[109]Patel T H, Darpe A K. Vibration response of a cracked rotor in presence of rotor-stator rub[J]. Journal of Sound and Vibration,2008,317(3-5):841-865.
[110]Rehman N, Mandic D P. Multivariate empirical mode decomposition[J]. Proceedings of the Royal Society A:Mathematical, Physical and Engineering Science,2009,0502:1471-2946.
[111]Niederreiter H. Random Number Generation and Quasi-Monte Carlo Methods[M]. Philadelphia: PA:Society for Industrial and Applied Mathematics,1992.
[112]Xiong X, Yang S, Gan C. A new procedure for extracting fault feature of multi-frequency signal from rotating machinery[J]. Mechanical Systems and Signal Processing,32:306-319.
[113]Bently D E, Yu J J, Goldman P, et al. Full Annular RUB in Mechanical Seals, Part I: Experimental Results[J]. International Journal of Rotating Machinery,2002,8(5):319-328.
[114]Choi Y-S. Dynamics of rotor rub in annular clearance with experimental evaluation [J]. Journal of Mechanical Science and Technology,1994,8(4):404-413.
[115]徐尉南,张文,许斌.柔性转子-柔性静子系统的同步全周碰摩分析[J].振动与冲击,2007,26(2)：1-5,9,171.
[116]Pennacchi P, Bachschmid N, Tanzi E. Light and short arc rubs in rotating machines: Experimental tests and modelling[J]. Mechanical Systems and Signal Processing,2009,23(7): 2205-2227.
[117]王正浩,袁惠群,范改燕.考虑轴初弯曲时转子系统局部碰摩的分叉与混沌行为[J].机械设计,2008,25(2)：3741.
[118]Cheng J, Yu D, Tang J, et al. Local rub-impact fault diagnosis of the rotor systems based on EMD[J]. Mechanism and Machine Theory,2009,44(4):784-791.
[119]熊炘,杨世锡,周晓峰.旋转机械振动信号的固有模式函数降噪方法[J].浙江大学学报(工学版),2011,45(8)：1376-1381.
[120]Huang N E, Wu Z, Long S R, et al. On instantaneous frequency[J]. Advances in Adaptive Data Analysis (AADA),2009,1(2):177-229.
[121]Antoni J. The spectral kurtosis:a useful tool for characterising non-stationary signals[J]. Mechanical Systems and Signal Processing,2006,20(2):282-307.
[122]Antoni J. Fast computation of the kurtogram for the detection of transient faults[J]. Mechanical Systems and Signal Processing,2007,21(1):108-124.
[123]Sohre J. Operating Problems with High-Speed Turbomachinery-Causes and Correction, in 23rd Annual Petroleum Mechanical Engineering Conference.1968:Denver, Colorado.
[124]Venkatasubramanian V, Rengaswamy R, Yin K, et al. A review of process fault detection and diagnosis part I:Quantitative model-based methods[J]. Computers and Chemical Engineering,2003, 27(3):293-311.
[125]钟秉林,黄仁.机械故障诊断学[M].北京：机械工业出版社,2006.
[126]陈长征,胡立新,周勃,等.设备振动分析与故障诊断技术[M].北京：科学出版社,2007.
[127]Nieddu L, Patrizi G. Formal methods in pattern recognition:A review[J]. European Journal of Operational Research,2000,120(3):459-495.
[128]Jain A K, Duin R P W, Mao J C. Statistical pattern recognition:A review[J]. leee Transactions on Pattern Analysis and Machine Intelligence,2000,22(1):4-37.
[129]Worden K, Staszewski W J, Hensman J J. Natural computing for mechanical systems research:A tutorial overview[J]. Mechanical Systems and Signal Processing,2011,25(1):4-111.
[130]Dahl G E, Yu D, Deng L, et al. Context-Dependent Pre-Trained Deep Neural, Networks for Large-Vocabulary Speech Recognition[J]. leee Transactions on Audio Speech and Language Processing,2012,20(1):30-42.
[131]Lin J C, Wu C H, Wei W L. Error Weighted Semi-Coupled Hidden Markov Model for Audio-Visual Emotion Recognition [J]. Ieee Transactions on Multimedia,2012,14(1):142-156.
[132]Slosarz P, Stanisz M, Boniecki P, et al. Artificial neural network analysis of ultrasound image for the estimation of intramuscular fat content in lamb muscle[J]. African Journal of Biotechnology, 2011,10(55):11792-11796.
[133]Madani K, leee. Artificial Neural Networks Based Image Processing & Pattern Recognition: From Concepts to Real-World Applications[M].2008 First International Workshops on Image Processing Theory, Tools and Applications 19-27,2008.
[134]Murthy V S, Gupta S, Mohanta D K. Digital image processing approach using combined wavelet hidden Markov model for well-being analysis of insulators[J]. let Image Processing,2011, 5(2):171-183.
[135]Ju Z J, Liu H H, Zhu X Y, et al. Dynamic Grasp Recognition Using Time Clustering, Gaussian Mixture Models and Hidden Markov Models[J]. Advanced Robotics,2009,23(10):1359-1371.
[136]Khan F N, Zhou Y D, Lau A P T, et al. Modulation format identification in heterogeneous fiber-optic networks using artificial neural networks[J]. Optics Express,2012,20(11): 12422-12431.
[137]Jin X, Huang B. Identification of switched Markov autoregressive eXogenous systems with hidden switching state[J]. Automatica,2012,48(2):436-441.
[138]Das N, Reddy J M, Sarkar R, et al. A statistical-topological feature combination for recognition of handwritten numerals[J]. Applied Soft Computing,2012,12(8):2486-2495.
[139]Solera-Urena R, Garcia-Moral A I, Pelaez-Moreno C, et al. Real-Time Robust Automatic Speech Recognition Using Compact Support Vector Machines[J]. Ieee Transactions on Audio Speech and Language Processing,2012,20(4):1347-1361.
[140]Wang B H, Huang H J, Wang X L. A novel text mining approach to financial time series forecasting[J]. Neurocomputing,2012,83:136-145.
[141]Fong S. Using Hierarchical Time Series Clustering Algorithm and Wavelet Classifier for Biometric Voice Classification[J]. Journal of Biomedicine and Biotechnology,2012,1-12.
[142]Widodo A, Yang B S. Support vector machine in machine condition monitoring and fault diagnosis[J]. Mechanical Systems and Signal Processing,2007,21(6):2560-2574.
[143]Wang Y, Kang S, Jiang Y, et al. Classification of fault location and the degree of performance degradation of a rolling bearing based on an improved hyper-sphere-structured multi-class support vector machine[J]. Mechanical Systems and Signal Processing,2012,29(0):404-414.
[144]MUSZYNSKA A. Rotor-to-stationary element rub-related vibration phenomena in rotating machinery:Literature survey[J]. The Shock and Vibration Digest,1989,21(3):3-11.
[145]Wang Q H, Zhang Y Y, Cai L, et al. Fault diagnosis for diesel valve trains based on non-negative matrix factorization and neural network ensemble[J]. Mechanical Systems and Signal Processing, 2009,23(5):1683-1695.
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